EEG-Based Driver Drowsiness Classification Using Support Vector Machine on Monotonous Road Driving Simulation

Ayu Lestari; Anizar Anizar; Nismah Panjaitan

doi:10.57152/malcom.v6i3.2776

Authors

Ayu Lestari Universitas Sumatera Utara
Anizar Anizar Universitas Sumatera Utara
Nismah Panjaitan Universitas Sumatera Utara

DOI:

https://doi.org/10.57152/malcom.v6i3.2776

Keywords:

Drowsiness Detection, EEG, Monotonous Driving, Observer Rated Sleepiness, Support Vector Machine

Abstract

Driver drowsiness is a critical road safety issue responsible for thousands of traffic fatalities annually, particularly on monotonous road environments such as toll highways. This research introduces an Electroencephalography (EEG)-based driver drowsiness classification model using a Support Vector Machine (SVM) algorithm, with Observer Rated Sleepiness (ORS) as the ground truth. Data were collected from 22 licensed drivers who completed a 90-minute monotonic driving simulation in an ergonomics laboratory using the Muse S portable EEG device. The features extracted from the EEG consisted of mean and standard deviation for delta, theta, alpha, and beta band power each (14 in total) per one-minute epoch, as well as relative band power for each band and two ratios: the theta/alpha (?/?) ratio and (theta+alpha)/beta ((?+?)/?) ratio. Each classification was carried out using an SVM with a Radial Basis Function (RBF) kernel, and a One vs Rest (OvR) multi-class strategy, and the generalization power was validated using Leave-One-Subject-Out Cross Validation (LOSO-CV), which exhibits subject-independent generalization. Results showed an average LOSO-CV accuracy of 71.97% (SD = 14.67%) with a Macro-F1 score of 0.7187. The ?/? ratio was the most discriminative feature, increasing from ?0.54 (alert) to 1.59 (severely drowsy).

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References

P. Philip and T. Åkerstedt, “Transport and industrial safety, how are they affected by sleepiness and sleep restriction?,” Oct. 2006. doi: 10.1016/j.smrv.2006.04.002.

R. N. Khushaba, S. Kodagoda, S. Lal, and G. Dissanayake, “Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm,” IEEE Trans. Biomed. Eng., vol. 58, no. 1, pp. 121–131, Jan. 2011, doi: 10.1109/TBME.2010.2077291.

P. K. Sivakumar, J. Selvaraj, K. Ramaraj, and A. Sahayadhas, “Analysis of alpha and theta band to detect driver drowsiness using electroencephalogram (Eeg) signals,” International Arab Journal of Information Technology, vol. 18, no. 4, pp. 578–584, Jul. 2021, doi: 10.34028/18/4/10.

T. Åkerstedt and M. Gillberg, “Subjective and objective sleepiness in the active individual,” International Journal of Neuroscience, vol. 52, no. 1–2, pp. 29–37, 1990, doi: 10.3109/00207459008994241.

A. W. Maclean, D. R. T. Davies, and K. Thiele, “Sleep The hazards and prevention of driving while sleepy,” 2003.

M. Doudou, A. Bouabdallah, and V. Berge-Cherfaoui, “Driver Drowsiness Measurement Technologies: Current Research, Market Solutions, and Challenges,” International Journal of Intelligent Transportation Systems Research, vol. 18, no. 2, pp. 297–319, May 2020, doi: 10.1007/s13177-019-00199-w.

Y. Zheng, Y. Ma, J. Cammon, S. Zhang, J. Zhang, and Y. Zhang, “A new feature selection approach for driving fatigue EEG detection with a modified machine learning algorithm,” Comput. Biol. Med., vol. 147, Aug. 2022, doi: 10.1016/j.compbiomed.2022.105718.

P. Thiffault and J. Bergeron, “Monotony of road environment and driver fatigue: a simulator study,” 2003.

C. T. Lin, R. C. Wu, S. F. Liang, W. H. Chao, Y. J. Chen, and T. P. Jung, “EEG-based drowsiness estimation for safety driving using independent component analysis,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 52, no. 12, pp. 2726–2738, 2005, doi: 10.1109/TCSI.2005.857555.

J. Ma, J. Gu, H. Jia, Z. Yao, and R. Chang, “The relationship between drivers’ cognitive fatigue and speed variability during monotonous daytime driving,” Front. Psychol., vol. 9, no. APR, Apr. 2018, doi: 10.3389/fpsyg.2018.00459.

T. Zhang, H. Wang, J. Chen, and E. He, “Detecting unfavorable driving states in electroencephalography based on a pca sample entropy feature and multiple classification algorithms,” Entropy, vol. 22, no. 11, pp. 1–13, Nov. 2020, doi: 10.3390/e22111248.

A. Setiawan, A. D. Wibawa, E. S. Pane, and M. H. Purnomo, “EEG-based Mental Fatigue Detection Using Cognitive Tests and RVM Classification,” 2019, Accessed: Aug. 12, 2025. [Online]. Available: 10.1109/ICAIIT.2019.8834509

F. Gharagozlou, A. Mazloumi, A. Nahvi, K. N. Toosi, and A. M. Nasrabadi, “Detecting Driver Mental Fatigue Based on EEG Alpha Power Changes during Simulated Driving,” 2015. [Online]. Available: http://ijph.tums.ac.ir

Q. Abbas and A. Alsheddy, “Driver fatigue detection systems using multi-sensors, smartphone, and cloud-based computing platforms: A comparative analysis,” Jan. 01, 2021, MDPI AG. doi: 10.3390/s21010056.

E. Wascher, H. Heppner, and S. Hoffmann, “Towards the measurement of event-related EEG activity in real-life working environments,” International Journal of Psychophysiology, vol. 91, no. 1, pp. 3–9, Jan. 2014, doi: 10.1016/j.ijpsycho.2013.10.006.

M. Karthaus, E. Wascher, and S. Getzmann, “Distraction in the driving simulator: An event-related potential (ERP) study with young, middle-aged, and older drivers,” Safety, vol. 7, no. 2, May 2021, doi: 10.3390/safety7020036.

M. Zokaei, M. J. Jafari, R. Khosrowabadi, A. Nahvi, S. Khodakarim, and M. Pouyakian, “Tracing the physiological response and behavioral performance of drivers at different levels of mental workload using driving simulators,” J. Safety Res., vol. 72, pp. 213–223, Feb. 2020, doi: 10.1016/j.jsr.2019.12.022.

A. Anund, C. Fors, J. Ihlström, and G. Kecklund, “An on-road study of sleepiness in split shifts among city bus drivers,” Accid. Anal. Prev., vol. 114, pp. 71–76, May 2018, doi: 10.1016/j.aap.2017.05.005.

C. Kaufmann, M. Frühwirth, D. Messerschmidt, M. Moser, A. Eichberger, and S. Arefnezhad, “Driving and tiredness: Results of the behaviour observation of a simulator study with special focus on automated driving,” Transactions on Transport Sciences, vol. 11, no. 2, pp. 51–63, Sep. 2020, doi: 10.5507/TOTS.2020.011.

B. Cheng, W. Zhang, Y. Lin, R. Feng, and X. Zhang, “Driver drowsiness detection based on multisource information,” Human Factors and Ergonomics in Manufacturing and Service Industries, vol. 22, no. 5, pp. 450–467, Sep. 2012, doi: 10.1002/hfm.20395.

E. Dong, G. Zhu, C. Chen, J. Tong, Y. Jiao, and S. Du, “Introducing chaos behavior to kernel relevance vector machine (RVM) for four-class EEG classification,” PLoS One, vol. 13, no. 6, Jun. 2018, doi: 10.1371/journal.pone.0198786.

N. V. Kimmatkar and V. B. Babu, “Human emotion classification from brain EEG signal using multimodal approach of classifier,” in ACM International Conference Proceeding Series, Association for Computing Machinery, Feb. 2018, pp. 9–13. doi: 10.1145/3193063.3193067.

K. Q. Shen, X. P. Li, C. J. Ong, S. Y. Shao, and E. P. V. Wilder-Smith, “EEG-based mental fatigue measurement using multi-class support vector machines with confidence estimate,” Clinical Neurophysiology, vol. 119, no. 7, pp. 1524–1533, Jul. 2008, doi: 10.1016/j.clinph.2008.03.012.

Y. J. Xiong, R. Zhang, C. Zhang, and X. L. Yu, “A novel estimation method of fatigue using EEG based on KPCA-SVM and complexity parameters,” in Applied Mechanics and Materials, 2013, pp. 965–969. doi: 10.4028/www.scientific.net/AMM.373-375.965.

M. I. Ahmad, I. Keller, D. A. Robb, and K. S. Lohan, “A framework to estimate cognitive load using physiological data,” Pers. Ubiquitous Comput., vol. 27, no. 6, pp. 2027–2041, Dec. 2023, doi: 10.1007/s00779-020-01455-7.

Y. Uchiyama et al., “Convergent validity of video-based observer rating of drowsiness, against subjective, behavioral, and physiological measures,” PLoS One, vol. 18, no. 5 May, May 2023, doi: 10.1371/journal.pone.0285557.

C. Ahlstrom, C. Fors, A. Anund, and D. Hallvig, “Video-based observer rated sleepiness versus self-reported subjective sleepiness in real road driving,” European Transport Research Review, vol. 7, no. 4, pp. 1–9, Dec. 2015, doi: 10.1007/s12544-015-0188-y.